We studied the impact of the problem decomposer by removing it completely from the training and testing process. We trained a student to answer sub-questions based on the intermediate steps directly meaning depending on what step question is asked, the problem solver learns to answer that step. If sub-question \(q^{(1)}\)  is asked, the model learns to answer \(s^{(1)}\) , and when \(q^{(2)}\)  is asked, the model learns to build the steps \(\lbrace s^{(1)}, s^{(2)}\rbrace \)  and not just \(s^{(2)}\) . This means that during the test time, a multistep question can range from 2 to 8 steps and the model builds the needed steps to reach the final answer \(a\) . This phenomenon can be thought of as curriculum learning [1]} where simple to more complex concepts are taught to the model and the learning happens gradually. However, in our case, the model did not learn to map solution steps iteratively; leading to a much worse performance during test time. The results demonstrating the need for the problem decomposer are summarised in Table REF .
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